01. Введение.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Глава 1. Введение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "# introduction.py\n",
    "\n",
    "import matplotlib as mpl\n",
    "# на этой стадии книги мы на самом деле еще не установили библиотеку matplotlib,\n",
    "# закомментируйте эти строки, если хотите\n",
    "from matplotlib import pyplot as plt\n",
    "mpl.style.use('ggplot')\n",
    "\n",
    "##########################\n",
    "#                        #\n",
    "# ПОИСК КЛЮЧЕВЫХ ЗВЕНЬЕВ #\n",
    "#                        #\n",
    "##########################\n",
    "\n",
    "users = [\n",
    "    { \"id\": 0, \"name\": \"Hero\" },\n",
    "    { \"id\": 1, \"name\": \"Dunn\" },\n",
    "    { \"id\": 2, \"name\": \"Sue\" },\n",
    "    { \"id\": 3, \"name\": \"Chi\" },\n",
    "    { \"id\": 4, \"name\": \"Thor\" },\n",
    "    { \"id\": 5, \"name\": \"Clive\" },\n",
    "    { \"id\": 6, \"name\": \"Hicks\" },\n",
    "    { \"id\": 7, \"name\": \"Devin\" },\n",
    "    { \"id\": 8, \"name\": \"Kate\" },\n",
    "    { \"id\": 9, \"name\": \"Klein\" },\n",
    "    { \"id\": 10, \"name\": \"Jen\" }\n",
    "]\n",
    "\n",
    "friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),\n",
    "               (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# сначала назначим каждому пользователю пустой список\n",
    "# свойство friends содержит друзей для пользователя user\n",
    "for user in users:\n",
    "    user[\"friends\"] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'friends': [], 'name': 'Hero', 'id': 0}\n"
     ]
    }
   ],
   "source": [
    "print(users[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# и затем заполним списки друзьями\n",
    "for i, j in friendships:\n",
    "    # это работает, потому что users[i] - это пользователь, чей id равен i\n",
    "    users[i][\"friends\"].append(users[j]) # добавить i как друга для j\n",
    "    users[j][\"friends\"].append(users[i]) # добавить j как друга для i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "def number_of_friends(user):\n",
    "    \"\"\"сколько друзей есть у пользователя user?\"\"\"\n",
    "    return len(user[\"friends\"]) # длина списка id друзей\n",
    "\n",
    "total_connections = sum(number_of_friends(user)  # общее число связей\n",
    "                        for user in users)       # 24\n",
    "\n",
    "print(total_connections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1818181818181817\n"
     ]
    }
   ],
   "source": [
    "num_users = len(users)                           # длина списка пользователей\n",
    "avg_connections = total_connections / num_users  # среднее число связей = 2.4\n",
    "print(avg_connections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "######################################\n",
    "#                                    #\n",
    "# АНАЛИТИКИ, КОТОРЫХ ВЫ ДОЛЖНЫ ЗНАТЬ #\n",
    "#                                    #\n",
    "######################################\n",
    "\n",
    "# список id друзей пользователя user (плохой вариант функции)\n",
    "def friends_of_friend_ids_bad(user):\n",
    "    # \"foaf\" - это аббревиатура для \"friend of a friend\", .т.е. друг конкретного друга\n",
    "    return [foaf[\"id\"]\n",
    "            for friend in user[\"friends\"]  # для каждого из друзей пользователя\n",
    "            for foaf in friend[\"friends\"]] # получить всех ЕГО друзей\n",
    "\n",
    "from collections import Counter # не загружается по умолчанию\n",
    "\n",
    "# не тот же самый\n",
    "def not_the_same(user, other_user):\n",
    "    \"\"\"два пользователя не одинаковые, если их ключи имеют разные id\"\"\"\n",
    "    return user[\"id\"] != other_user[\"id\"]\n",
    "\n",
    "# не друзья\n",
    "def not_friends(user, other_user):\n",
    "    \"\"\"other_user - не друг, если он не принадлежит user[\"friends\"], т. е.\n",
    "    если он not_the_same (не тот же что и все люди в user[\"friends\"])\"\"\"\n",
    "    return all(not_the_same(friend, other_user)\n",
    "               for friend in user[\"friends\"])\n",
    "\n",
    "# список id друзей пользователя user\n",
    "def friends_of_friend_ids(user):\n",
    "    return Counter(foaf[\"id\"]\n",
    "                   for friend in user[\"friends\"]  # для каждого моего друга\n",
    "                   for foaf in friend[\"friends\"]  # подсчитать ИХ друзей,\n",
    "                   if not_the_same(user, foaf)    # которые не являются мной\n",
    "                   and not_friends(user, foaf))   # и не мои друзья"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 2, 5: 1})\n"
     ]
    }
   ],
   "source": [
    "print(friends_of_friend_ids(users[3])) # Counter({0: 2, 5: 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# интересующие темы\n",
    "interests = [\n",
    "    (0, \"Hadoop\"), (0, \"Big Data\"), (0, \"HBase\"), (0, \"Java\"),\n",
    "    (0, \"Spark\"), (0, \"Storm\"), (0, \"Cassandra\"),\n",
    "    (1, \"NoSQL\"), (1, \"MongoDB\"), (1, \"Cassandra\"), (1, \"HBase\"),\n",
    "    (1, \"Postgres\"), (2, \"Python\"), (2, \"scikit-learn\"), (2, \"scipy\"),\n",
    "    (2, \"numpy\"), (2, \"statsmodels\"), (2, \"pandas\"), (3, \"R\"), (3, \"Python\"),\n",
    "    (3, \"statistics\"), (3, \"regression\"), (3, \"probability\"),\n",
    "    (4, \"machine learning\"), (4, \"regression\"), (4, \"decision trees\"),\n",
    "    (4, \"libsvm\"), (5, \"Python\"), (5, \"R\"), (5, \"Java\"), (5, \"C++\"),\n",
    "    (5, \"Haskell\"), (5, \"programming languages\"), (6, \"statistics\"),\n",
    "    (6, \"probability\"), (6, \"mathematics\"), (6, \"theory\"),\n",
    "    (7, \"machine learning\"), (7, \"scikit-learn\"), (7, \"Mahout\"),\n",
    "    (7, \"neural networks\"), (8, \"neural networks\"), (8, \"deep learning\"),\n",
    "    (8, \"Big Data\"), (8, \"artificial intelligence\"), (9, \"Hadoop\"),\n",
    "    (9, \"Java\"), (9, \"MapReduce\"), (9, \"Big Data\")\n",
    "]\n",
    "\n",
    "# аналитики, которым нравится целевая тема target_interest\n",
    "def data_scientists_who_like(target_interest):\n",
    "    return [user_id\n",
    "            for user_id, user_interest in interests\n",
    "            if user_interest == target_interest]\n",
    "\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {'Hadoop': [0, 9], 'libsvm': [4], 'Java': [0, 5, 9], 'machine learning': [4, 7], 'Storm': [0], 'Python': [2, 3, 5], 'neural networks': [7, 8], 'numpy': [2], 'Haskell': [5], 'artificial intelligence': [8], 'decision trees': [4], 'probability': [3, 6], 'statsmodels': [2], 'Big Data': [0, 8, 9], 'MapReduce': [9], 'scikit-learn': [2, 7], 'programming languages': [5], 'statistics': [3, 6], 'Cassandra': [0, 1], 'NoSQL': [1], 'pandas': [2], 'Mahout': [7], 'theory': [6], 'Postgres': [1], 'deep learning': [8], 'HBase': [0, 1], 'scipy': [2], 'R': [3, 5], 'regression': [3, 4], 'Spark': [0], 'C++': [5], 'MongoDB': [1], 'mathematics': [6]})\n"
     ]
    }
   ],
   "source": [
    "# id пользователей по значению темы\n",
    "# ключи - это интересующие темы,\n",
    "# значения - это списки из id пользователей, интересующихся этой темой\n",
    "user_ids_by_interest = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    user_ids_by_interest[interest].append(user_id)\n",
    "    \n",
    "print(user_ids_by_interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {0: ['Hadoop', 'Big Data', 'HBase', 'Java', 'Spark', 'Storm', 'Cassandra'], 1: ['NoSQL', 'MongoDB', 'Cassandra', 'HBase', 'Postgres'], 2: ['Python', 'scikit-learn', 'scipy', 'numpy', 'statsmodels', 'pandas'], 3: ['R', 'Python', 'statistics', 'regression', 'probability'], 4: ['machine learning', 'regression', 'decision trees', 'libsvm'], 5: ['Python', 'R', 'Java', 'C++', 'Haskell', 'programming languages'], 6: ['statistics', 'probability', 'mathematics', 'theory'], 7: ['machine learning', 'scikit-learn', 'Mahout', 'neural networks'], 8: ['neural networks', 'deep learning', 'Big Data', 'artificial intelligence'], 9: ['Hadoop', 'Java', 'MapReduce', 'Big Data']})\n"
     ]
    }
   ],
   "source": [
    "# идентификаторы тем по идентификатору пользователя\n",
    "# ключи - это id пользователей, значения - списки тем для конкретного id\n",
    "interests_by_user_id = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    interests_by_user_id[user_id].append(interest)\n",
    "    \n",
    "print(interests_by_user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 2})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# наиболее общие интересующие темы с пользователем user\n",
    "def most_common_interests_with(user):\n",
    "    return Counter(interested_user_id\n",
    "        for interest in interests_by_user_id[user[\"id\"]]\n",
    "        for interested_user_id in user_ids_by_interest[interest]\n",
    "        if interested_user_id != user[\"id\"])\n",
    "\n",
    "most_common_interests_with(users[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KyxPU2hoT5sqCdzOdS6QjHAEAIpbLZTHCRK/OTotcLstlntF/3UznEukIRwCAiOVw+BUX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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7acf2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "#                        #\n",
    "# ЗАРПЛАТЫ И ОПЫТ РАБОТЫ #\n",
    "#                        #\n",
    "##########################\n",
    "\n",
    "# зарплаты и стаж\n",
    "salaries_and_tenures = [(83000, 8.7), (88000, 8.1),\n",
    "                        (48000, 0.7), (76000, 6),\n",
    "                        (69000, 6.5), (76000, 7.5),\n",
    "                        (60000, 2.5), (83000, 10),\n",
    "                        (48000, 1.9), (63000, 4.2)]\n",
    "\n",
    "# Зависимость заработной платы от опыта работы\n",
    "def make_chart_salaries_by_tenure():\n",
    "    tenures = [tenure for salary, tenure in salaries_and_tenures]\n",
    "    salaries = [salary for salary, tenure in salaries_and_tenures]\n",
    "    plt.scatter(tenures, salaries)\n",
    "    plt.xlabel(\"tenure\")\n",
    "    plt.ylabel(\"salary\")\n",
    "    plt.show()\n",
    "    \n",
    "make_chart_salaries_by_tenure()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.7: 48000.0,\n",
       " 1.9: 48000.0,\n",
       " 2.5: 60000.0,\n",
       " 4.2: 63000.0,\n",
       " 6: 76000.0,\n",
       " 6.5: 69000.0,\n",
       " 7.5: 76000.0,\n",
       " 8.1: 88000.0,\n",
       " 8.7: 83000.0,\n",
       " 10: 83000.0}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# зарплата в зависимости от стажа\n",
    "# ключи - это годы, значения - это списки зарплат для каждого стажа\n",
    "salary_by_tenure = defaultdict(list)\n",
    "\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    salary_by_tenure[tenure].append(salary)\n",
    "\n",
    "# средняя зарплата в зависимости от стажа\n",
    "# ключи - это годы, каждое значение - это средняя зарплата по этому стажу\n",
    "average_salary_by_tenure = {\n",
    "    tenure : sum(salaries) / len(salaries)\n",
    "    for tenure, salaries in salary_by_tenure.items()\n",
    "}\n",
    "\n",
    "average_salary_by_tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'более пяти': 79166.66666666667,\n",
       " 'между двумя и пятью': 61500.0,\n",
       " 'менее двух': 48000.0}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# стажная группа\n",
    "def tenure_bucket(tenure):\n",
    "    if tenure < 2: return \"менее двух\"\n",
    "    elif tenure < 5: return \"между двумя и пятью\"\n",
    "    else: return \"более пяти\"\n",
    "\n",
    "salary_by_tenure_bucket = defaultdict(list)\n",
    "\n",
    "# зарплата в зависимости от стажной группы\n",
    "# ключи = стажные группы, значения = списки зарплат в этой группе\n",
    "# словарь содержит списки зарплат, соответствующие каждой стажной группе\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    bucket = tenure_bucket(tenure)\n",
    "    salary_by_tenure_bucket[bucket].append(salary)\n",
    "\n",
    "# средняя зарплата по группе\n",
    "# ключи = стажные группы, значения = средняя зарплата по этой группе\n",
    "average_salary_by_bucket = {\n",
    "  tenure_bucket : sum(salaries) / len(salaries)\n",
    "  for tenure_bucket, salaries in salary_by_tenure_bucket.items()\n",
    "}\n",
    "\n",
    "average_salary_by_bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "############################\n",
    "#                          #\n",
    "# ОПЛАТА ПРЕМИУМ-АККАУНТОВ #\n",
    "#                          #\n",
    "############################\n",
    "\n",
    "# предсказать платежи, исходя из стажа\n",
    "def predict_paid_or_unpaid(years_experience):\n",
    "    if years_experience < 3.0: return \"оплачено\"\n",
    "    elif years_experience < 8.5: return \"не оплачено\"\n",
    "    else: return \"оплачено\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "big 3\n",
      "java 3\n",
      "data 3\n",
      "python 3\n",
      "learning 3\n",
      "neural 2\n",
      "networks 2\n",
      "probability 2\n",
      "scikit-learn 2\n",
      "cassandra 2\n",
      "machine 2\n",
      "statistics 2\n",
      "hbase 2\n",
      "hadoop 2\n",
      "r 2\n",
      "regression 2\n"
     ]
    }
   ],
   "source": [
    "###################\n",
    "#                 #\n",
    "# ПОПУЛЯРНЫЕ ТЕМЫ #\n",
    "#                 #\n",
    "###################\n",
    "\n",
    "# слова и частотности\n",
    "words_and_counts = Counter(word\n",
    "                           for user, interest in interests\n",
    "                           for word in interest.lower().split())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for word, count in words_and_counts.most_common():\n",
    "    if count > 1:\n",
    "        print(word, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# ПОИСК КЛЮЧЕВЫХ ЗВЕНЬЕВ\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "\n",
    "    print(\"всего связей\", total_connections)\n",
    "    print(\"количество пользователей\", num_users)\n",
    "    print(\"среднее количество связей\", total_connections / num_users)\n",
    "    print()\n",
    "\n",
    "    # create a list (user_id, number_of_friends)\n",
    "    num_friends_by_id = [(user[\"id\"], number_of_friends(user))\n",
    "                         for user in users]\n",
    "\n",
    "    print(\"пользователи, отсортированные по количеству друзей:\")\n",
    "    print(sorted(num_friends_by_id,\n",
    "                 key=lambda pair: pair[1],                       # по количеству друзей\n",
    "                 reverse=True))                                  # от большего к меньшему\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# АНАЛИТИКИ, КОТОРЫХ ВЫ ДОЛЖНЫ ЗНАТЬ\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "\n",
    "    print(\"друзья друзей для пользователя 0 (плохой вариант):\", friends_of_friend_ids_bad(users[0]))\n",
    "    print(\"друзья друзей для пользователя 3:\", friends_of_friend_ids(users[3]))\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# ЗАРПЛАТЫ И ОПЫТ РАБОТЫ\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "    print(\"средняя зарплата по стажу\", average_salary_by_tenure)\n",
    "    print(\"средняя зарплата по стажной группе\", average_salary_by_bucket)"
   ]
  }
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